Insight Studies on Metal-Organic Framework Nanofibrous Membrane Adsorption and Activation for Heavy Metal Ions Removal from Aqueous Solution
Bibliographic record
Abstract
Electrospun nanofiber composite membranes containing water-stable metal-organic frameworks (MOFs) particles (Zr-based MOF-808) supported on polyacrylonitrile (PAN) nanofiber synthesized via co-electrospinning have been prepared. MOF particles were dispersed in the organic polymer, and their subsequent presence was inferred by scanning electron microscopy. Membrane performance in heavy metal ion adsorption in batch filtration was evaluated on the basis of Cd 2+ and Zn 2+ ions sequestration. The adsorption capacities of the pristine MOF and the MOF composite membrane revealed that MOF particles in the membrane could be accessed for adsorption in the hydrophilic PAN membranes. The maximum adsorption capacities were 225.05 and 287.06 mg g –1 for Cd 2+ and Zn 2+, respectively. Conventional thermal activation of pristine MOF and composite membrane revealed a crystal downsizing, while “hydractivation” produced an expanded MOF with enhanced adsorption potentials. The PAN/MOF-808 “hydractivated” composite membrane could treat 580 mL of Cd, whereas the conventional vacuum-activated composite treated 464 mL. The high separation performance and reusability of the membranes and the outstanding water stability of the MOFs suggested the developed membrane as a potential candidate for water treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".